Multi-Objective Optimization
Multi-objective optimization seeks designs that balance several conflicting objectives, such as performance and cost, at once. Instead of a single optimum it returns a Pareto front of non-dominated solutions, where improving one objective requires sacrificing another, and evolutionary methods such as the NSGA-II genetic algorithm are commonly used to find it for expensive black-box models.
Pareto fronts for amine CO2 capture processes (2013)
Lee and co-workers ran NSGA-II over ten design and operating variables of an Aspen Plus capture and compression model, maximizing net plant power and minimizing capital cost for each of three amine solvents (Lee et al. 2013). The fronts exposed a minimum capital cost for 90% capture, a ceiling on net power and diminishing returns in efficiency for added capital, and comparing fronts across solvents gave a fairer ranking than evaluating all solvents in one fixed design. Summary: lee2013.